A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects

Applied Intelligence - The17 Sustainable Development Goals (SDGs) established by the United Nations Agenda 2030 constitute a global blueprint agenda and instrument for peace and prosperity...     

Eicosapentaenoic acid (20∶5n-3) from the marine microalgaPhaeodactylum tricornutum

Eicosapentaenoic acid (EPA, 20∶5n-3) was obtained from the marine microalgaePhaeodactylum tricornutum by a three-step process: fatty acid extraction by direct saponification of biomass, polyunsaturated fatty acid (PUFA) concentration by formation of urea inclusion compounds, and EPA isolation by semipreparative high-performance liquid chromatography (HPLC). Alternatively, EPA was obtained by a similar two-step process without the PUFA concentration step by the urea method. Direct saponification of biomass was carried out with two solvents that contained KOH for lipid saponification. An increase in yield was obtained because the problems associated with emulsion formation were avoided by separating the biomass from the soap solution before adding hexane for extraction of insaponifiables. The most efficient solvent, ethanol (96%) at 60°C for 1 h, extracted 98.3% of EPA. PUFA were concentrated by the urea method with a urea/fatty acid ratio of 4∶1 at a crystallization temperature of 28°C and by using methanol and ethanol as urea solvents. An EPA concentration ratio of 1.73 (55.2∶31.9) and a recover yield of 78.6% were obtained with methanol as the urea solvent. This PUFA concentrate was used to obtain 93.4% pure EPA by semipreparative HPLC with a reverse-phase, C₁₈, 10 mm i.d.×25-cm column and methanol/water (1% acetic acid), 80∶20 w/w, as the mobile phase. Eighty-five percent of EPA loaded was recovered, and 65.7% of EPA present inP. tricornutum biomass was recovered in highly pure form by this three-step downstream process. Alternatively, 93.6% pure EPA was isolated from the fatty acid extract (without the PUFA concentration step) with 100% EPA recovery yield. This two-step process increases the overall EPA yield to 98.3%, but it is only possible to obtain 20% as much EPA as that obtained by three-step downstream processing.     

Effect of low‐temperature plasma and chitosan treatment on wool dyeing with Acid Red 27

This study examines in detail the influence of low‐temperature plasma and biopolymer chitosan treatments on wool dyeability. Wool knitted fabrics were treated and characterized by whiteness and shrink‐resistance measurements. Surface modification was assessed by contact‐angle measurements of human hair fibers, which were used as a model to study the wetting properties of the treated wool knitted fabrics. The dyeing behavior was assessed from the diffusion mechanism point of view. The dyeing kinetics were measured at two different pHs (4.2 and 6.5) and three different temperatures (60, 85, and 100°C) to gain information about the contribution of the surface modification treatment to the dyeing mechanism. The exhaustion and reflectance data were compared, and the apparent diffusion coefficients were calculated. On the basis of the obtained results, a model for the dyeing mechanism of the chitosan treated wool was proposed. When treated with chitosan, the polymer sheath spread on the surface of the fibers acted as a predominant dyeing site in very short dyeing times, thus interacting with the dye and in later stages imparting the dye to the wool fiber. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 97: 2204–2214, 2005     

I3WSN: Industrial Intelligent Wireless Sensor Networks for indoor environments

Sensor Web Enablement (SWE) technologies have been successfully applied to a great variety of outdoor scenarios but, in practical terms, little effort has been applied for indoor environments, and even less in the field of industrial applications. This article presents an intelligent SWE application for an indoor and industrial scenario, with the aim of improving and increasing the levels of human safety. The base low-level architecture is built on top of wireless sensor networks (WSN) connected to a Sensor Observation Service (SOS). Higher layers in the architecture include services that make real time decisions based on the collected data. Both simulation and experimental results are presented. The paper shows the viability of our approach in terms of performance, scalability, modularity and safety.     

Iron species incorporated over different silica supports for the heterogeneous photo-Fenton oxidation of phenol

Iron-containing catalysts have been prepared following different synthesis routes and silica supports (amorphous, zeolitic and mesostructured materials). Activity and stability of these materials were assessed on the photo-Fenton degradation of phenolic aqueous solutions using near UV irradiation (higher than 313 nm) at room temperature and initial neutral pH. Their catalytic performance was monitored in terms of phenol and total organic carbon (TOC) conversions. Aromatic compounds and carboxylic acids as by-products coming from incomplete mineralization of phenol as well as the efficiency of each catalytic system in the use of the oxidant were also studied. Stability of the materials throughout the photo-Fenton reaction was evaluated in terms of metal leachibility. Activity and stability depend on the environment of iron species and features of silica support. The evolution of pH with the reaction time and their relationship with TOC degradation and leaching degree has been discussed. A nanocomposite material of crystalline iron oxides supported over mesostructured SBA-15 material is shown the most successful catalyst for degradation of phenolic aqueous solutions by photo-Fenton processes, achieving an outstanding overall catalytic performance accompanied with a noteworthy stability.     

Semiquantitative analysis of thiophenic compounds in light cycle oil (LCO) using C NMR spectroscopy

S-methyltetrafluoroborate salts of the thiophenic compounds (CH₃-S⁺:BF₄⁻) present in LCO petroleum fractions were obtained and analyzed by ¹H and ¹³C NMR spectroscopy. The methylation of the samples was carried out using 99.5% ¹³C enriched methyl iodine, to improve the sensitivity of the technique. The amount of the methylated derivatives was determined by the internal standard method; using dioxane as a reference, 37 sulphur compounds were detected. Among them, benzo[b]thiophene, dibenzo[b,d]thiophene, and several isomers of methyl, dimethyl and trimethyl[b]benzothiophenes were the most abundant. With this research, it was demonstrated that NMR spectroscopy can be used to analyze thiophenic compounds from petroleum medium fractions.     

A finite volume–finite difference method with a stiff ordinary differential equation solver for advection–diffusion–reaction equation

A model for atmospheric pollutant transport is proposed considering an advection–diffusion–reaction equation. A splitting method is used to decouple the advection, diffusion and reaction parts. A scheme based on finite volume, finite difference and backward differentiation formula is used for solving an atmospheric transport-chemistry problem.     

Multi-objective optimization with an adaptive resonance theory-based estimation of distribution algorithm

The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have an intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work, we put forward the argument that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and a hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.